Cause effect pairs in machine learning. ([2019])

• Record Type: Book
• Further Information: Note: Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu, editors.
• Editors: Guyon, Isabelle; Statnikov, Alexander Romanovich; Batu, Berna Bakir
• Contents: 1. The cause-effect problem: motivation, ideas, and popular misconceptions -- 2. Evaluation methods of cause-effect pairs -- 3. Learning Bivariate Functional Causal Models -- 4. Discriminant Learning Machines -- 5. Cause-Effect Pairs in Time Series with a Focus on Econometrics -- 6. Beyond cause-effect pairs -- 7. Results of the Cause-Effect Pair Challenge -- 8. Non-linear Causal Inference using Gaussianity Measures -- 9. From Dependency to Causality: A Machine Learning Approach -- 10. Pattern-based Causal Feature Extraction -- 11. Training Gradient Boosting Machines using Curve-fitting and Information-theoretic Features for Causal Direction Detection -- 12. Conditional distribution variability measures for causality detection -- 13. Feature importance in causal inference for numerical and categorical variables -- 14. Markov Blanket Ranking using Kernel-based Conditional Dependence Measures.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource (xvi, 372 pages), illustrations (some color)
• Subjects: 006.31; Machine learning; Artificial intelligence; Causation; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030218102; 3030218104
• Related ISBNs: 9783030218096
• Notes: Note: Includes bibliographical references.; Note: Online resource; title from PDF title page (SpringerLink, viewed October 30, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.